Introducing the ‘active search’ method for iterative virtual screening
A method is introduced for sequential similarity searching for active compounds. Given a set of known actives and a screening database, a strategy is devised to optimally rank test compounds by observing the outcome of each iteration before selecting the next compound. This ‘active search’ approach is based upon Bayesian decision theory. A typical ranking procedure used in virtual compound screening corresponds to a myopic approximation to the optimal strategy. Exploratory active search represents a less-myopic approach and is shown to accurately identify a variety of active compounds in iterative virtual screening trials on 120 compound classes. Source code and data for the active search approach presented herein is made freely available.
KeywordsActive search Iterative virtual screening Bayesian decision theory
- 6.Garnett R, Krishnamurthy Y, Xiong X, Schneider J, Mann RP (2012) Bayesian optimal active search and surveying. In: Langford J (ed) Proceedings of the 29th international conference on machine learning (ICML 2012). Pineau J, pp 1239–1246Google Scholar
- 7.Robert C (2007) The Bayesian choice. Springer, New YorkGoogle Scholar
- 8.Garnett R Active Search Toolbox for MATLAB. https://github.com/rmgarnett/active_search
- 15.Molecular Operating Environment (MOE) (2012) Chemical Computing Group, Montreal, CanadaGoogle Scholar
- 16.MACCS Structural Keys (2011) Accelrys, San Diego, CAGoogle Scholar